Robust speech recognition using missing feature theory and vector quantization

Abstract

This paper addresses the problem of speech recognition in noisy conditions when low complexity is required like in embedded systems. In such systems, vector quantization is generally used to reduce the complexity of the recognition systems (e.g. HMMs). A novel approach for vector quantization based on the missing data theory is proposed. This approach allows to increase the robustness of the system against the noise perturbations with only a small increase of the computational requirements. The proposed algorithm is composed of two parts. The first part consists in dividing the spectral temporal features of the noisy signal into two subspaces: the unreliable (or missing) features and the reliable (or present) features. The second part of the proposed approach consists in defining a robust distance measure for vector quantization that compensates for the unreliable features. The proposed approach obtains similar results in noisy conditions than a more classical approach that consists in adapting the codebook of the vector quantization to the noisy conditions using model compensation. However the computation requirements are lower in the proposed approach and it is more suitable for a low complexity speech recognition system.